Identifying cases of congestive heart failure from administrative data: a validation study using primary care patient records

Abstract

Introduction To determine if using a combination of hospital administrative data and ambulatory care physician billings can accurately identify patients with congestive heart failure (CHF), we tested 9 algorithms for identifying individuals with CHF from administrative data. Methods The validation cohort against which the 9 algorithms were tested combined data from a random sample of adult patients from EMRALD, an electronic medical record database of primary care physicians in Ontario, Canada, and data collected in 2004/05 from a random sample of primary care patients for a study of hypertension. Algorithms were evaluated on sensitivity, specificity, positive predictive value, area under the curve on the ROC graph and the combination of likelihood ratio positive and negative. Results We found that that one hospital record or one physician billing followed by a second record from either source within one year had the best result, with a sensitivity of 84.8% and a specificity of 97.0%. Conclusion Population prevalence of CHF can be accurately measured using combined administrative data from hospitalization and ambulatory care.

Measuring the association between marginalization and multimorbidity in Ontario, Canada: A cross-sectional study

“…Also, the ICES algorithms are not perfect and there is potential for false positives and false negatives in identifying morbidities. However, the validated algorithms for most conditions are generally robust [22][23][24] and with a sample size this large, any minor inaccuracies unless widespread should not be an issue. Also, we do not have an exhaustive list of morbidities, nor does a gold standard presently exist for better measuring multimorbidity with the use of health administrative databases.…”

Testing, infection and complication rates of COVID-19 among people with a recent history of homelessness in Ontario, Canada: a retrospective cohort study

“…Data sources used to define participants, outcomes and covariates included the following: the DAD and the SDS database; the NACRS; the OMHRS; the Ontario Laboratories Information System; the ICES Registered Persons Database demographic and postal year data sets; the OHIP claims database; the Immigration, Refugee and Citizenship Canada Permanent Resident Database; the Ontario Drug Benefit (ODB) Database; and several ICES-derived population-surveillance data sets, including the Chronic Obstructive Pulmonary Disease Database, 26 the Ontario Asthma Database, 27 the Ontario Diabetes Database, 28 the Congestive Heart Failure Database 29 and the Ontario Hypertension Database. 30 These databases are further described in Appendix 1, Supplemental Tables S1 and S2.…”

The prevalence and health status of people with developmental disabilities in provincial prisons in Ontario, Canada: A retrospective cohort study

“…The present authors applied validated algorithms (Antoniou, Zagorski, Loutfy, Strike, & Glazier, 2011; Gershon et al., 2009a, 2009b; Hux, Ivis, Flintoft, & Bica, 2002; Schultz, Rothwell, Chen, & Tu, 2013; Tu, Campbell, Chen, Cauch‐Dudek, & McAlister, 2007) to define the proportion of people with a diagnosis of diabetes, hypertension, chronic obstructive pulmonary disease (COPD), asthma, congestive heart failure (CHF) and HIV infection. Based on prior research by project team members, the present authors identified people with a diagnosis in the past two years of any mental disorder, including psychotic disorders and substance‐related disorders, in physician billing (OHIP), hospitalization (Canadian Institute of Health Information [CIHI] Discharge Abstracts Database [DAD], Ontario Mental Health Reporting Systems [OMHRS]) and emergency department (CIHI National Ambulatory Care Reporting System [NACRS]) administrative data (Lunsky et al., 2012).…”